The past 25 years of genomics research first revealed which genes are encoded by the human genome and then a detailed catalogue of human genome variation associated with many diseases. Despite this, the function of many genes and gene regulatory elements remains poorly characterized, which limits our ability to apply these insights to human disease. The advent of new CRISPR functional genomics tools allows for scalable and multiplexable characterization of genes and gene regulatory elements encoded by the human genome. These approaches promise to reveal mechanisms of gene function and regulation, and to enable exploration of how genes work together to modulate complex phenotypes.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Doudna, J. A. & Charpentier, E. Genome editing. The new frontier of genome engineering with CRISPR–Cas9. Science 346, 1258096 (2014).
Nakamura, M., Gao, Y., Dominguez, A. A. & Qi, L. S. CRISPR technologies for precise epigenome editing. Nat. Cell Biol. 23, 11–22 (2021).
Holtzman, L. & Gersbach, C. A. Editing the epigenome: reshaping the genomic landscape. Annu. Rev. Genomics Hum. Genet. 19, 43–71 (2018).
Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR–Cas9. Nat. Rev. Genet. 16, 299–311 (2015).
Doench, J. G. Am I ready for CRISPR? A user’s guide to genetic screens. Nat. Rev. Genet. 19, 67–80 (2018). This Review describes several important practical aspects for running CRISPR screens, including crucial quality control metrics to monitor at different screening steps.
Hanna, R. E. & Doench, J. G. Design and analysis of CRISPR–Cas experiments. Nat. Biotechnol. 38, 813–823 (2020). This review highlights data analysis strategies and pipelines for CRISPR screens that are a fundamental part of screen interpretation and analysis not covered in the present Review.
Knott, G. J. & Doudna, J. A. CRISPR–Cas guides the future of genetic engineering. Science 361, 866–869 (2018).
Wang, H., La Russa, M. & Qi, L. S. CRISPR/Cas9 in genome editing and beyond. Annu. Rev. Biochem. 85, 227–264 (2016).
Pickar-Oliver, A. & Gersbach, C. A. The next generation of CRISPR–Cas technologies and applications. Nat. Rev. Mol. Cell Biol. 20, 490–507 (2019).
Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).
Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).
Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016). Together with Dixit et al. (2016) and Adamson et al. (2016), this seminal scFG CRISPR paper demonstrates the potential for using pooled single-cell CRISPR screens as a discovery platform.
Warren, H. R. et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat. Genet. 49, 403–415 (2017).
Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013).
Horlbeck, M. A. et al. Mapping the genetic landscape of human cells. Cell 174, 953–967.e22 (2018). This large-scale CRISPRi genetic interaction map demonstrates the utility of this approach in mammalian cells and serves as a broad resource and blueprint for future studies.
Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786–793 (2019).
Costanzo, M. et al. Global genetic networks and the genotype-to-phenotype relationship. Cell 177, 85–100 (2019).
Domingo, J., Baeza-Centurion, P. & Lehner, B. The causes and consequences of genetic interactions (Eeistasis). Annu. Rev. Genomics Hum. Genet. 20, 433–460 (2019).
Tian, R. et al. CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons. Neuron 104, 239–255.e12 (2019). This paper presents the first large-scale screen performed in iPS cell-derived cells, providing a template for future studies and revealing valuable information about neuronal differentiation and function.
O’Loughlin, T. A. & Gilbert, L. A. Functional genomics for cancer research: applications in vivo and in vitro. Annu. Rev. Cancer Biol. 3, 345–363 (2019).
Chow, R. D. & Chen, S. Cancer CRISPR screens in vivo. Trends Cancer 4, 349–358 (2018).
Li, C. & Kasinski, A. L. In vivo cancer-based functional genomics. Trends Cancer 6, 1002–1017 (2020).
Weber, J., Braun, C. J., Saur, D. & Rad, R. In vivo functional screening for systems-level integrative cancer genomics. Nat. Rev. Cancer 20, 573–593 (2020).
Winters, I. P., Murray, C. W. & Winslow, M. M. Towards quantitative and multiplexed in vivo functional cancer genomics. Nat. Rev. Genet. 19, 741–755 (2018).
Jasin, M. & Haber, J. E. The democratization of gene editing: insights from site-specific cleavage and double-strand break repair. DNA Repair 44, 6–16 (2016).
Rouet, P., Smih, F. & Jasin, M. Introduction of -strand breaks into the genome of mouse cells by expression of a rare-cutting endonuclease. Mol. Cell. Biol. 14, 8096–8106 (1994).
Yeh, C. D., Richardson, C. D. & Corn, J. E. Advances in genome editing through control of DNA repair pathways. Nat. Cell Biol. 21, 1468–1478 (2019).
Carroll, D. Genome engineering with zinc-finger nucleases. Genetics 188, 773–782 (2011).
Chandrasegaran, S. & Carroll, D. Origins of programmable nucleases for genome engineering. J. Mol. Biol. 428, 963–989 (2016).
Urnov, F. D., Rebar, E. J., Holmes, M. C., Zhang, H. S. & Gregory, P. D. Genome editing with engineered zinc finger nucleases. Nat. Rev. Genet. 11, 636–646 (2010).
Joung, J. K. & Sander, J. D. TALENs: a widely applicable technology for targeted genome editing. Nat. Rev. Mol. Cell Biol. 14, 49–55 (2013).
Ousterout, D. G. & Gersbach, C. A. The development of TALE nucleases for biotechnology. Methods Mol. Biol. Clifton NJ 1338, 27–42 (2016).
Tebas, P. et al. Gene editing of CCR5 in autologous CD4 T cells of persons infected with HIV. N. Engl. J. Med. 370, 901–910 (2014).
Hoban, M. D. et al. Correction of the sickle cell disease mutation in human hematopoietic stem/progenitor cells. Blood 125, 2597–2604 (2015).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT00842634 (2019).
US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT02702115 (2020).
Beerli, R. R., Segal, D. J., Dreier, B. & Barbas, C. F. Toward controlling gene expression at will: specific regulation of the erbB-2/HER-2 promoter by using polydactyl zinc finger proteins constructed from modular building blocks. Proc. Natl Acad. Sci. USA 95, 14628–14633 (1998).
Konermann, S. et al. Optical control of mammalian endogenous transcription and epigenetic states. Nature 500, 472–476 (2013).
Gaj, T., Gersbach, C. A. & Barbas, C. F. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 31, 397–405 (2013).
Mohr, S., Bakal, C. & Perrimon, N. Genomic screening with RNAi: results and challenges. Annu. Rev. Biochem. 79, 37–64 (2010).
Boettcher, M. & McManus, M. T. Choosing the right tool for the job: RNAi, TALEN, or CRISPR. Mol. Cell 58, 575–585 (2015).
Jackson, A. L. & Linsley, P. S. Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat. Rev. Drug Discov. 9, 57–67 (2010).
Jackson, A. L. et al. Expression profiling reveals off-target gene regulation by RNAi. Nat. Biotechnol. 21, 635–637 (2003).
Acevedo-Arozena, A. et al. ENU mutagenesis, a way forward to understand gene function. Annu. Rev. Genomics Hum. Genet. 9, 49–69 (2008).
Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).
Brockmann, M. et al. Genetic wiring maps of single-cell protein states reveal an off-switch for GPCR signalling. Nature 546, 307–311 (2017).
Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).
Kampmann, M. CRISPRi and CRISPRa screens in mammalian cells for precision biology and medicine. ACS Chem. Biol. 13, 406–416 (2018).
Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR–Cas9 complex. Nature 517, 583–588 (2015).
Xu, X. & Qi, L. S. A CRISPR–dCas toolbox for genetic engineering and synthetic biology. J. Mol. Biol. 431, 34–47 (2019).
Anzalone, A. V., Koblan, L. W. & Liu, D. R. Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors. Nat. Biotechnol. 38, 824–844 (2020).
Shalem, O. et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014). Together with Shalem et al. (2014), this paper presents a seminal genome-scale CRISPR screen demonstrating the enormous potential of CRISPR for next-generation functional genomics.
Morgens, D. W., Deans, R. M., Li, A. & Bassik, M. C. Systematic comparison of CRISPR–Cas9 and RNAi screens for essential genes. Nat. Biotechnol. 34, 634–636 (2016).
le Sage, C. et al. Dual direction CRISPR transcriptional regulation screening uncovers gene networks driving drug resistance. Sci. Rep. 7, 17693 (2017).
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).
Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic ras. Cell 168, 890–903.e15 (2017).
Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, M. D. C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR–guide RNA library. Nat. Biotechnol. 32, 267–273 (2014).
Zhou, Y. et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487–491 (2014).
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017). This seminal publication of the DepMap data enables comparative large-scale analysis of CRISPR screening data across diverse cell types.
Chan, E. M. et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 568, 551–556 (2019).
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
Kampmann, M. CRISPR-based functional genomics for neurological disease. Nat. Rev. Neurol. 16, 465–480 (2020).
Mair, B. et al. Essential gene profiles for human pluripotent stem cells identify uncharacterized genes and substrate dependencies. Cell Rep. 27, 599–615.e12 (2019).
Ihry, R. J. et al. Genome-scale CRISPR screens identify human pluripotency-specific genes. Cell Rep. 27, 616–630.e6 (2019).
Puschnik, A. S., Majzoub, K., Ooi, Y. S. & Carette, J. E. A CRISPR toolbox to study virus–host interactions. Nat. Rev. Microbiol. 15, 351–364 (2017).
Marceau, C. D. et al. Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens. Nature 535, 159–163 (2016).
Jeng, E. E. et al. Systematic identification of host cell regulators of Legionella pneumophila pathogenesis using a genome-wide CRISPR screen. Cell Host Microbe 26, 551–563.e6 (2019).
Kory, N. et al. SFXN1 is a mitochondrial serine transporter required for one-carbon metabolism. Science 362, eaat9528 (2018).
Birsoy, K. et al. An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell 162, 540–551 (2015).
Lou, K. et al. KRASG12C inhibition produces a driver-limited state revealing collateral dependencies. Sci. Signal. 12, eaaw9450 (2019).
Jost, M. & Weissman, J. S. CRISPR approaches to small molecule target identification. ACS Chem. Biol. 13, 366–375 (2018).
Jost, M. et al. Combined CRISPRi/a-based chemical genetic screens reveal that rigosertib is a microtubule-destabilizing agent. Mol. Cell 68, 210–223.e6 (2017).
Zimmermann, M. et al. CRISPR screens identify genomic ribonucleotides as a source of PARP-trapping lesions. Nature 559, 285–289 (2018).
Huang, A., Garraway, L. A., Ashworth, A. & Weber, B. Synthetic lethality as an engine for cancer drug target discovery. Nat. Rev. Drug Discov. 19, 23–38 (2020).
Kabir, S. et al. The CUL5 ubiquitin ligase complex mediates resistance to CDK9 and MCL1 inhibitors in lung cancer cells. eLife 8, e44288 (2019).
Fomicheva, M. & Macara, I. G. Genome-wide CRISPR screen identifies noncanonical NF-κB signaling as a regulator of density-dependent proliferation. eLife 9, e63603 (2020).
Wang, L. et al. High-throughput functional genetic and compound screens identify targets for senescence induction in cancer. Cell Rep. 21, 773–783 (2017).
Schmid-Burgk, J. L. et al. A genome-wide CRISPR (clustered regularly interspaced short palindromic repeats) screen identifies NEK7 as an essential component of NLRP3 inflammasome activation. J. Biol. Chem. 291, 103–109 (2016).
Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018).
Findlay, G. M., Boyle, E. A., Hause, R. J., Klein, J. & Shendure, J. Saturation editing of genomic regions by multiplex homology-directed repair. Nature 513, 120–123 (2014).
Hanna, R. E. et al. Massively parallel assessment of human variants with base editor screens. Cell 184, 1064–1080.e20 (2021).
Cuella-Martin, R. et al. Functional interrogation of DNA damage response variants with base editing screens. Cell 184, 1081–1097.e19 (2021).
Hess, G. T. et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13, 1036–1042 (2016).
Ma, L. et al. CRISPR–Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy. Proc. Natl Acad. Sci. USA 114, 11751–11756 (2017).
Liang, J. R. et al. A genome-wide ER-phagy screen highlights key roles of mitochondrial metabolism and ER-resident UFMylation. Cell 180, 1160–1177.e20 (2020).
Li, Q. V. et al. Genome-scale screens identify JNK/JUN signaling as a barrier for pluripotency exit and endoderm differentiation. Nat. Genet. 51, 999–1010 (2019).
Pusapati, G. V. et al. CRISPR screens uncover genes that regulate target cell sensitivity to the morphogen sonic hedgehog. Dev. Cell 44, 113–129.e8 (2018).
Torres, S. E. et al. Ceapins block the unfolded protein response sensor ATF6α by inducing a neomorphic inter-organelle tether. eLife 8, e46595 (2019).
Potting, C. et al. Genome-wide CRISPR screen for PARKIN regulators reveals transcriptional repression as a determinant of mitophagy. Proc. Natl Acad. Sci. USA 115, E180–E189 (2018).
Henriksson, J. et al. Genome-wide CRISPR screens in T helper cells reveal pervasive crosstalk between activation and differentiation. Cell 176, 882–896.e18 (2019).
Dixon, G. et al. QSER1 protects DNA methylation valleys from de novo methylation. Science 372, eabd0875 (2021).
Gretarsson, K. H. & Hackett, J. A. Dppa2 and Dppa4 counteract de novo methylation to establish a permissive epigenome for development. Nat. Struct. Mol. Biol. 27, 706–716 (2020).
Rauch, J. N. et al. Tau internalization is regulated by 6-O sulfation on heparan sulfate proteoglycans (HSPGs). Sci. Rep. 8, 6382 (2018).
Park, R. J. et al. A genome-wide CRISPR screen identifies a restricted set of HIV host dependency factors. Nat. Genet. 49, 193–203 (2017).
Park, J. S. et al. A FACS-based genome-wide CRISPR screen reveals a requirement for COPI in Chlamydia trachomatis invasion. iScience 11, 71–84 (2019).
Liu, S. J. et al. CRISPRi-based genome-scale identification of functional long noncoding RNA lociin human cells. Science 355, aah7111 (2017).
Chen, J. J. et al. Compromised function of the ESCRT pathway promotes endolysosomal escape of tau seeds and propagation of tau aggregation. J. Biol. Chem. 294, 18952–18966 (2019).
Mendelsohn, B. A. et al. A high-throughput screen of real-time ATP levels in individual cells reveals mechanisms of energy failure. PLoS Biol. 16, e2004624 (2018).
Bayraktar, E. C. et al. Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism. Nat. Metab. 2, 487–498 (2020).
Wainberg, M. et al. A genome-wide atlas of co-essential modules assigns function to uncharacterized genes. Nat. Genet. 53, 638–649 (2021).
Yogodzinski, C., Arab, A., Pritchard, J. R., Goodarzi, H. & Gilbert, L. A. A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy. bioRxiv https://doi.org/10.1101/2021.01.08.425918 (2021).
Zhao, B., Rao, Y., Gilbert, L. & Pritchard, J. A common genetic architecture enables the lossy compression of large CRISPR libraries. bioRxiv https://doi.org/10.1101/2020.12.18.423506 (2020).
Cui, Y. et al. CRISP-view: a database of functional genetic screens spanning multiple phenotypes. Nucleic Acids Res. 49, D848–D854 (2021).
McKinley, K. L. & Cheeseman, I. M. Large-scale analysis of CRISPR/Cas9 cell-cycle knockouts reveals the diversity of p53-dependent responses to cell-cycle defects. Dev. Cell 40, 405–420.e2 (2017).
Wang, C., Lu, T., Emanuel, G., Babcock, H. P. & Zhuang, X. Imaging-based pooled CRISPR screening reveals regulators of lncRNA localization. Proc. Natl Acad. Sci. USA 116, 10842–10851 (2019).
de Groot, R., Lüthi, J., Lindsay, H., Holtackers, R. & Pelkmans, L. Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR–Cas9 gene perturbation screens. Mol. Syst. Biol. 14, e8064 (2018).
Strezoska, Ž. et al. High-content analysis screening for cell cycle regulators using arrayed synthetic crRNA libraries. J. Biotechnol. 251, 189–200 (2017).
Feldman, D. et al. Optical pooled screens in human cells. Cell 179, 787–799.e17 (2019).
Kanfer, G. et al. Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes. J. Cell Biol. 220, e202006180 (2021).
Yan, X. et al. High-content imaging-based pooled CRISPR screens in mammalian cells. J. Cell Biol. 220, e202008158 (2021).
Wheeler, E. C. et al. Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors. Nat. Methods 17, 636–642 (2020).
Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).
Xie, S., Duan, J., Li, B., Zhou, P. & Hon, G. C. Multiplexed engineering and analysis of combinatorial enhancer activity in single cells. Mol. Cell 66, 285–299.e5 (2017).
Adamson, B., Norman, T. M., Jost, M. & Weissman, J. S. Approaches maximize sgRNA-barcode coupling Perturb-seq screens. bioRxiv https://doi.org/10.1101/298349 (2018).
Replogle, J. M. et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat. Biotechnol. 38, 954–961 (2020).
Jin, X. et al. In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes. Science 370, eaaz6063 (2020).
Dhainaut, M. et al. Perturb-map enables CRISPR genomics with spatial resolution and identifies regulators of tumor immune composition. bioRxiv https://doi.org/10.1101/2021.07.13.451021 (2021).
Liu, J. et al. Pooled library screening with multiplexed Cpf1 library. Nat. Commun. 10, 3144 (2019).
Gonçalves, E. et al. Minimal genome-wide human CRISPR–Cas9 library. Genome Biol. 22, 40 (2021).
Peets, E. M. et al. Minimized double guide RNA libraries enable scale-limited CRISPR/Cas9 screens. bioRxiv https://doi.org/10.1101/859652 (2019).
Schraivogel, D. et al. Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat. Methods 17, 629–635 (2020).
Srivatsan, S. R. et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367, 45–51 (2020).
Henkel, L., Rauscher, B., Schmitt, B., Winter, J. & Boutros, M. Genome-scale CRISPR screening at high sensitivity with an empirically designed sgRNA library. BMC Biol. 18, 174 (2020).
Datlinger, P. et al. Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. Nat. Methods 18, 635–642 (2021).
Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).
Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Richer, A. L., Riemondy, K. A., Hardie, L. & Hesselberth, J. R. Simultaneous measurement of biochemical phenotypes and gene expression in single cells. Nucleic Acids Res. 48, e59 (2020).
Wroblewska, A. et al. Protein barcodes enable high-dimensional single-cell CRISPR screens. Cell 175, 1141–1155.e16 (2018).
Quinn, J. J. et al. Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts. Science 371, 6532 (2021).
Chan, M. M. et al. Molecular recording of mammalian embryogenesis. Nature 570, 77–82 (2019).
Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376.e17 (2019).
Frangieh, C. J. et al. Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nat. Genet. 53, 332–341 (2021).
Calin, G. A. & Croce, C. M. MicroRNA signatures in human cancers. Nat. Rev. Cancer 6, 857–866 (2006).
Alvarez-Garcia, I. & Miska, E. A. MicroRNA functions in animal development and human disease. Development 132, 4653–4662 (2005).
Schmitz, S. U., Grote, P. & Herrmann, B. G. Mechanisms of long noncoding RNA function in development and disease. Cell. Mol. Life Sci. 73, 2491–2509 (2016).
Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).
Joung, J. et al. Genome-scale activation screen identifies a lncRNA locus regulating a gene neighbourhood. Nature 548, 343–346 (2017).
Wallace, J. et al. Genome-wide CRISPR–Cas9 screen identifies microRNAs that regulate myeloid leukemia cell growth. PLoS ONE 11, e0153689 (2016).
Kurata, J. S. & Lin, R.-J. MicroRNA-focused CRISPR–Cas9 library screen reveals fitness-associated miRNAs. RNA 24, 966–981 (2018).
Covarrubias, S. et al. CRISPR/Cas-based screening of long non-coding RNAs (lncRNAs) in macrophages with an NF-κB reporter. J. Biol. Chem. 292, 20911–20920 (2017).
Bester, A. C. et al. An integrated genome-wide CRISPRa approach to functionalize lncRNAs in drug resistance. Cell 173, 649–664.e20 (2018).
Esposito, R. et al. Hacking the cancer genome: profiling therapeutically actionable long non-coding RNAs using CRISPR–Cas9 screening. Cancer Cell 35, 545–557 (2019).
Liu, Y. et al. Genome-wide screening for functional long noncoding RNAs in human cells by Cas9 targeting of splice sites. Nat. Biotechnol. 36, 1203–1210 (2018).
Phelan, J. D. & Staudt, L. M. CRISPR-based technology to silence the expression of IncRNAs. Proc. Natl Acad. Sci. USA 117, 8225–8227 (2020).
Zhu, S. et al. Genome-scale deletion screening of human long non-coding RNAs using a paired-guide RNA CRISPR library. Nat. Biotechnol. 34, 1279–1286 (2016).
Reber, S. et al. CRISPR-Trap: a clean approach for the generation of gene knockouts and gene replacements in human cells. Mol. Biol. Cell 29, 75–83 (2018).
Wolter, J. M. et al. Cas9 gene therapy for Angelman syndrome traps Ube3a-ATS long non-coding RNA. Nature 587, 281–284 (2020).
Bergadà-Pijuan, J., Pulido-Quetglas, C., Vancura, A. & Johnson, R. CASPR, an analysis pipeline for single and paired guide RNA CRISPR screens, reveals optimal target selection for long non-coding RNAs. Bioinformatics 36, 1673–1680 (2020).
Liu, Y., Liu, Z., Cao, Z. & Wei, W. Reply to: Fitness effects of CRISPR/Cas9-targeting of long noncoding RNA genes. Nat. Biotechnol. 38, 577–578 (2020).
Horlbeck, M. A., Liu, S. J., Chang, H. Y., Lim, D. A. & Weissman, J. S. Fitness effects of CRISPR/Cas9-targeting of long noncoding RNA genes. Nat. Biotechnol. 38, 573–576 (2020).
Tam, V. et al. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 20, 467–484 (2019).
Giral, H., Landmesser, U. & Kratzer, A. Into the wild: GWAS exploration of non-coding RNAs. Front. Cardiovasc. Med. 5, 181 (2018).
Montefiori, L. E. et al. A promoter interaction map for cardiovascular disease genetics. eLife 7, e35788 (2018).
Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017).
Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).
Chiou, J. et al. Interpreting type 1 diabetes risk with genetics and single-cell epigenomics. Nature 594, 398–402 (2021).
Fulco, C. P. et al. Systematic mapping of functional enhancer–promoter connections with CRISPR interference. Science 354, 769–773 (2016).
Sanjana, N. E. et al. High-resolution interrogation of functional elements in the noncoding genome. Science 353, 1545–1549 (2016).
Cho, S. W. et al. Promoter of lncRNA gene PVT1 is a tumor-suppressor DNA boundary element. Cell 173, 1398–1412.e22 (2018).
Tycko, J. et al. Mitigation of off-target toxicity in CRISPR–Cas9 screens for essential non-coding elements. Nat. Commun. 10, 4063 (2019).
Klann, T. S. et al. Genome-wide annotation of gene regulatory elements linked to cell fitness. bioRxiv https://doi.org/10.1101/2021.03.08.434470 (2021).
Gasperini, M. et al. A genome-wide framework for mapping gene regulation via cellular genetic screens. Cell 176, 377–390.e19 (2019). This paper outlines an approach that has been widely adopted to assign relationships between non-coding enhancer/silencer regions of the genome and gene expression.
Klann, T. S. et al. CRISPR–Cas9 epigenome editing enables high-throughput screening for functional regulatory elements in the human genome. Nat. Biotechnol. 35, 561–568 (2017).
Fulco, C. P. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019).
Simeonov, D. R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017).
Hilton, I. B. et al. Epigenome editing by a CRISPR–Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510–517 (2015).
Kearns, N. A. et al. Functional annotation of native enhancers with a Cas9–histone demethylase fusion. Nat. Methods 12, 401–403 (2015).
Li, K. et al. Interrogation of enhancer function by enhancer-targeting CRISPR epigenetic editing. Nat. Commun. 11, 485 (2020).
Takahashi, K. & Yamanaka, S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126, 663–676 (2006).
Masserdotti, G., Gascón, S. & Götz, M. Direct neuronal reprogramming: learning from and for development. Development 143, 2494–2510 (2016).
Kuzmin, E. et al. Systematic analysis of complex genetic interactions. Science 360, eaao1729 (2018).
Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507–519 (2005).
Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).
Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).
Lehner, B., Crombie, C., Tischler, J., Fortunato, A. & Fraser, A. G. Systematic mapping of genetic interactions in Caenorhabditis elegans identifies common modifiers of diverse signaling pathways. Nat. Genet. 38, 896–903 (2006).
Ashworth, A. & Lord, C. J. Synthetic lethal therapies for cancer: what’s next after PARP inhibitors? Nat. Rev. Clin. Oncol. 15, 564–576 (2018).
Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017).
Bassik, M. C. et al. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152, 909–922 (2013).
Du, D. et al. Genetic interaction mapping in mammalian cells using CRISPR interference. Nat. Methods 14, 577–580 (2017).
Roguev, A. et al. Quantitative genetic-interaction mapping in mammalian cells. Nat. Methods 10, 432–437 (2013).
Rosenbluh, J. et al. Genetic and proteomic interrogation of lower confidence candidate genes reveals signaling networks in β-catenin-active cancers. Cell Syst. 3, 302–316.e4 (2016).
Shen, J. P. et al. Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14, 573–576 (2017).
Wong, A. S. L. et al. Multiplexed barcoded CRISPR–Cas9 screening enabled by CombiGEM. Proc. Natl Acad. Sci. USA 113, 2544–2549 (2016).
DeWeirdt, P. C. et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat. Biotechnol. 39, 94–104 (2021).
Najm, F. J. et al. Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36, 179–189 (2018).
Liu, Y. et al. CRISPR activation screens systematically identify factors that drive neuronal fate and reprogramming. Cell Stem Cell 23, 758–771.e8 (2018).
Dixit, A., Kuksenko, O., Feldman, D. & Regev, A. Shuffle-seq: en masse combinatorial encoding for n-way genetic interaction screens. bioRxiv https://doi.org/10.1101/861443 (2019).
DeWeirdt, P. C. et al. Genetic screens in isogenic mammalian cell lines without single cell cloning. Nat. Commun. 11, 752 (2020).
Aregger, M. et al. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism. Nat. Metab. 2, 499–513 (2020).
Gonatopoulos-Pournatzis, T. et al. Genetic interaction mapping and exon-resolution functional genomics with a hybrid Cas9–Cas12a platform. Nat. Biotechnol. 38, 638–648 (2020).
Boettcher, M. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36, 170–178 (2018).
Zalatan, J. G. et al. Engineering complex synthetic transcriptional programs with CRISPR RNA scaffolds. Cell 160, 339–350 (2015).
Cleary, B. & Regev, A. The necessity and power of random, under-sampled experiments in biology. Cornell University https://arxiv.org/abs/2012.12961 (2020).
Cleary, B., Cong, L., Cheung, A., Lander, E. S. & Regev, A. Efficient generation of transcriptomic profiles by random composite measurements. Cell 171, 1424–1436.e18 (2017).
Tian, R. et al. Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis. Nat. Neurosci. 24, 1020–1034 (2021).
Konermann, S. et al. Transcriptome engineering with RNA-targeting type VI-D CRISPR effectors. Cell 173, 665–676.e14 (2018).
Wessels, H.-H. et al. Massively parallel Cas13 screens reveal principles for guide RNA design. Nat. Biotechnol. 38, 722–727 (2020).
Abudayyeh, O. O. et al. RNA targeting with CRISPR–Cas13. Nature 550, 280–284 (2017).
Wilson, C., Chen, P. J., Miao, Z. & Liu, D. R. Programmable m6A modification of cellular RNAs with a Cas13-directed methyltransferase. Nat. Biotechnol. 38, 1431–1440 (2020).
Li, J. et al. Targeted mRNA demethylation using an engineered dCas13b–ALKBH5 fusion protein. Nucleic Acids Res. 48, 5684–5694 (2020).
Kampmann, M., Bassik, M. C. & Weissman, J. S. Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells. Proc. Natl Acad. Sci. USA 110, E2317–E2326 (2013).
Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015).
Jost, M. et al. CRISPR-based functional genomics in human dendritic cells. eLife 10, e65856 (2021).
Keys, H. R. & Knouse, K. A. A genome-wide screen in the mouse liver reveals sex-specific and cell non-autonomous regulation of cell fitness. bioRxiv https://doi.org/10.1101/2021.01.30.428976 (2021).
Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175, 1958–1971.e15 (2018).
Hultquist, J. F. et al. A Cas9 ribonucleoprotein platform for functional genetic studies of HIV–host interactions in primary human T cells. Cell Rep. 17, 1438–1452 (2016).
Schumann, K. et al. Generation of knock-in primary human T cells using Cas9 ribonucleoproteins. Proc. Natl Acad. Sci. USA 112, 10437–10442 (2015).
Schumann, K. et al. Functional CRISPR dissection of gene networks controlling human regulatory T cell identity. Nat. Immunol. 21, 1456–1466 (2020).
Roth, T. L. et al. Reprogramming human T cell function and specificity with non-viral genome targeting. Nature 559, 405–409 (2018).
Roth, T. L. et al. Pooled knockin targeting for genome engineering of cellular immunotherapies. Cell 181, 728–744.e21 (2020).
Gate, R. E. et al. Mapping gene regulatory networks of primary CD4+ T cells using single-cell genomics and genome engineering. bioRxiv https://doi.org/10.1101/678060 (2019).
Ting, P. Y. et al. Guide Swap enables genome-scale pooled CRISPR–Cas9 screening in human primary cells. Nat. Methods 15, 941–946 (2018).
Cortez, J. T. et al. CRISPR screen in regulatory T cells reveals modulators of Foxp3. Nature 582, 416–420 (2020).
Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015).
Dong, M. B. et al. Systematic immunotherapy target discovery using genome-scale in vivo CRISPR screens in CD8 T cells. Cell 178, 1189–1204.e23 (2019).
Kwart, D. et al. A large panel of isogenic APP and PSEN1 mutant human iPSC neurons reveals shared endosomal abnormalities mediated by APP β-CTFs, not Aβ. Neuron 104, 256–270.e5 (2019).
Nugent, A. A. et al. TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge. Neuron 105, 837–854.e9 (2020).
Andreone, B. J. et al. Alzheimer’s-associated PLCγ2 is a signaling node required for both TREM2 function and the inflammatory response in human microglia. Nat. Neurosci. 23, 927–938 (2020).
Renner, H. et al. A fully automated high-throughput workflow for 3D-based chemical screening in human midbrain organoids. eLife 9, e52904 (2020).
Brandenberg, N. et al. High-throughput automated organoid culture via stem-cell aggregation in microcavity arrays. Nat. Biomed. Eng. 4, 863–874 (2020).
L.A.G. is supported by a NIH New Innovator Award (DP2 CA239597), a Pew–Stewart Scholars for Cancer Research award as well as the Goldberg–Benioff Endowed Professorship in Prostate Cancer Translational Biology.
L.A.G. has filed patents on CRISPR functional genomics and is a co-founder of Chroma Medicine. The other authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
BioGRID ORCS: https://orcs.thebiogrid.org/
Cancer Dependency Map (DepMap): https://depmap.org/portal/
Co-essentiality network analysis: http://coessentiality.net
(Clustered regularly interspaced short palindromic repeats). A family of DNA sequences containing short repetitions that are found in prokaryotic organisms as a form of immunity against viruses together with the Cas family of enzymes.
- Genome-wide association studies
(GWAS). Large-scale genome-wide single-nucleotide polymorphism (SNP) analyses comparing genetic variants between a disease population and a control population to identify genetic loci associated with altered disease risk.
(CRISPR-associated protein 9). An RNA-guided DNA endonuclease involved in bacterial immunity that has been co-opted for use in mammalian genetic engineering.
(Dead Cas9). A catalytically inactive form of Cas9 generated by engineering loss-of-function mutations of the endonuclease domains (D10A and H840A).
- Fluorescence-activated cell sorting
(FACS). A method for sorting cells based on their intrinsic properties such as size, shape and fluorescent intensity downstream of a reporter or fluorophore-linked antibody.
- Genetic interactions
The sets of functional relationships between genes, which can be used to identify epistatic or synthetic lethal gene interactions.
- Induced pluripotent stem cells
(iPS cells). Cells reprogrammed from somatic cells with the ability to self-renew by dividing as well as the ability to differentiate into any cell type in the adult organism, a property known as pluripotency.
- Single-nucleotide polymorphism
(SNP). A variation in a single nucleotide in a DNA sequence.
About this article
Cite this article
Przybyla, L., Gilbert, L.A. A new era in functional genomics screens. Nat Rev Genet (2021). https://doi.org/10.1038/s41576-021-00409-w